Progressive collapse of steel structures exposed to fire: A critical review

Y Cao, J Jiang, Y Lu, W Chen, J Ye - journal of constructional steel research, 2023 - Elsevier
A state-of-the-art review is presented on design and research related to progressive
collapse of steel structures under fire conditions. The influence of load ratios, strength of …

Predicting real-time deformation of structure in fire using machine learning with CFD and FEM

Z Ye, SC Hsu - Automation in Construction, 2022 - Elsevier
Real-time prediction of structural safety conditions is critical to firefighting teams during
building fire rescue operations. This paper presents a numerical data-based machine …

Real-time prediction of key monitoring physical parameters for early warning of fire-induced building collapse

W Ji, GQ Li, S Zhu - Computers & Structures, 2022 - Elsevier
This paper proposes a real-time prediction method for key monitoring physical parameters
(KMPPs) for early warning of fire-induced building collapse using machine learning. Since …

[HTML][HTML] Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces

Y Zeng, X Zhang, L Su, X Wu, H Xinyan - Case Studies in Thermal …, 2022 - Elsevier
Fire modelling is a common practice in building fire safety analysis, but it is costly. This work
develops an AI software, Intelligent Fire Engineering Tool (IFETool), to speed up the fire …

Rapid visual simulation of the progressive collapse of regular reinforced concrete frame structures based on machine learning and physics engine

S Wang, X Cheng, Y Li, X Song, R Guo, H Zhang… - Engineering …, 2023 - Elsevier
Assessing the collapse region in the progressive collapse of buildings is one of the
important issues in urban planning for disaster recovery. A rapid visual simulation method …

RAGN-L: a stacked ensemble learning technique for classification of fire-resistant columns

AÖ Çiftçioğlu - Expert Systems with Applications, 2024 - Elsevier
One of the main challenges in using reinforced concrete materials in structures is to
comprehend their fire resistance. The assessment of fire resistance can be performed in a …

[HTML][HTML] AIoT-enabled digital twin system for smart tunnel fire safety management

X Zhang, Y Jiang, X Wu, Z Nan, Y Jiang, J Shi… - Developments in the …, 2024 - Elsevier
High traffic flow in a confined tunnel makes fire safety a critical issue. This paper proposed a
digital twin framework for tunnel fire safety management in real-time, driven by dynamic …

FAST-AlertNet: Early warning fire-induced collapse of large-span steel truss structures

J Li, GQ Li, S Zhu - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Large-span steel trusses are often adopted as roof structures of public and industrial
buildings. Besides significant property loss, the unexpected and sudden collapse of large …

Development of modular and reusable AI models for fast predicting fire behaviour of steel columns in structural systems

J Qiu, L Jiang - Engineering Structures, 2023 - Elsevier
It is important to model the local failure of structural members as well as the global
responses of a structural system when it is exposed to fire. The local failure of columns such …

Smart building fire safety design driven by artificial intelligence

Y Zeng, X Huang - Interpretable Machine Learning for the Analysis Design …, 2024 - Elsevier
The recent rise of numerical fire modeling has revealed the performance of building fire
safety and code. However, high-fidelity fire simulation is costly and difficult to analyze. This …